Identifying constitutive articles of cumulative dissertation theses by bilingual text similarity. Evaluation of similarity methods on a new short text task

Abstract

Cumulative dissertations are doctoral theses comprised of multiple published articles. For studies of publication activity and citation impact of early career researchers it is important to identify these articles and link them to their associated theses. Using a new benchmark dataset, this paper reports on experiments of measuring the bilingual textual similarity between, on the one hand, titles and keywords of doctoral theses, and, on the other hand, articles’ titles and abstracts. The tested methods are cosine similarity and L1 distance in the Vector Space Model (VSM) as baselines, the language-indifferent methods Latent Semantic Analysis (LSA) and trigram similarity, and the language-aware methods fastText and Random Indexing (RI). LSA and RI, two supervised methods, were trained on a purposively collected bilingual scientific parallel text corpus. The results show that the VSM baselines and the RI method perform best but that the VSM method is unsuitable for cross-language similarity due to its inherent monolingual bias.

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

“…FastText: is a free public-source library created by the Facebook AI Research team for learning WE and classification [44,45]. For each word, FastText generates a word vector that contains both the term's meaning and its context in the document.…”

Algorithmic identification of Ph.D. thesis-related publications: a proof-of-concept study

“…For the first stage, the goal is to rule out as many unlikely matches as possible while discarding as few actual matches as possible with relatively simple rules. We only briefly summarize this stage, as a detailed description is already available in Donner (2021b). Scopus records are filtered (a) by author name similarity for all authors affiliated with Germany and (b) publication year range.…”

“…The second criterion is textual similarity. As this is a considerably complex task in its own right, a separate study for the selection of suitable methods was carried out in Donner (2021b) and the reader is referred to this study for a detailed description of the methods. To summarize, the difficulties relate to the sparsity of the textual information, the bilingual nature of the texts, and domain specificity of the texts.…”

Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

“…In a jobmatching model, understanding context, relationships between words, and deeper meanings are required [19][20] [21]. The SI often has to limit the number of dimensions (semantic concepts) used to represent documents (difficult to interpret) [22][23] [24][25], Sensitivity to changes in documents [14] [26], and cognitive abilities [27] [16], or aspects of the job candidate's personality.…”